Directional Breeding Generates Distinct Genetic Diversity in Hybrid Turf Bermudagrass as Probed with Simple Sequence Repeat Markers

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Desalegn Serba US Department of Agriculture, Agriculture Research Service, US Arid-Land Agricultural Research Center, Maricopa, AZ 85138, USA

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Tilin Fang Plant and Soil Sciences Department, Oklahoma State University, Stillwater, OK 74078, USA

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Yanqi Wu Plant and Soil Sciences Department, Oklahoma State University, Stillwater, OK 74078, USA

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Abstract

Bermudagrass (Cynodon spp.) is a drought-resistant warm-season turfgrass adapted to the southern and transitional zones in the United States. Multiple hybrid cultivars have been developed and released for use as turfgrass, and others are still undergoing development. Increasing genetic diversity of commercial cultivars is vital to stress tolerance. A DNA profiling study of 21 experimental selections from the Oklahoma State University turfgrass breeding program and 11 cultivars was conducted using 51 simple sequence repeat primer pairs across the bermudagrass genome. A pairwise genetic relationship analysis of the genotypes using 352 polymorphic bands showed genetic similarity coefficients ranging from 0.59 to 0.89. The average pairwise population differentiation values were 0.012 for the 11 cultivars and 0.169 for the 21 selections. A cluster analysis using the unweighted paired group with the arithmetic average method grouped the entries into six clusters. A correlation analysis identified different levels of pairwise genetic relationships among the entries that largely reflected parental relationship. Directional breeding and selection for cold hardiness or drought resistance created progeny that had distinct genetic diversity in the tested bermudagrasses. It is evident that an increase in genetic diversity of the existing cultivar pool with the release of one or more experimental selections for commercial use will strengthen and improve bermudagrass systems.

Bermudagrass (Cynodon spp.) is a warm-season grass well-adapted to the tropical, subtropical, and warm climates (Beard 1972). Bermudagrass comprises cross-pollinated species with self-fertility of up to 3% (Burton and Hart 1967; Tan et al. 2014a). The genus includes a ploidy series of diploid, triploid, tetraploid, pentaploid, and hexaploid with a base chromosome number of x = 9 (Wu et al. 2006). Common bermudagrass [C. dactylon (L.) Pers.], African bermudagrass (C. transvaalensis Burtt-Davy) (Harlan and de Wet 1969), and interspecific hybrids of these two are widely used as turfgrass in the United States and other countries (Beard 1973; Taliaferro 2003). Common bermudagrass is both a tetraploid (2n = 4x = 36) and hexaploid (2n = 6x = 54) (Wu et al. 2005), whereas African bermudagrass is a diploid (2n = 2x = 18). Interspecific hybrids of the two species are generally considered the most economically important and widely used for home lawns, sports fields, and golf courses (Beard 2002). Intraploidy-level and interploidy-level crosses can produce viable seeds (Taliaferro 2003). The C. dactylon var. dactylon has three races: a tropical race that forms loose turf with short stature; a temperate race that appears to be similar to tropical but is denser and more winter-hardy; and a seleucidus race that has a very coarse texture, thick stolon, and rhizomes and is more cold-tolerant than the other two (Turgeon 2005).

Bermudagrasses are widely used on athletic fields (football, baseball, and soccer), public parks, tennis courts, and golf courses because of its drought and heat tolerance, high traffic tolerance, and excellent recovery potential after wear damage (Kowalewski et al. 2015). Triploid interspecific turf bermudagrass hybrids (2n = 3x = 27) that are vegetatively propagated are popular in the turf industry, partly because of their aesthetic beauty and extreme uniformity on golf courses, sports fields, and home lawns (Hanna and Anderson 2008). Other characteristics that make bermudagrass an attractive choice include its perennial sod-forming nature, exceptional spreading ability, tolerance to low mowing, and adaptation to a wide range of growing conditions (Taliaferro et al. 2004a).

The amazing adaptability and aggressive and dynamic nature of bermudagrass have long been recognized worldwide (Harlan and de Wet 1969). Through evolution and population processes, bermudagrass has been able to spread across multiple environments and geographic locations (Taliaferro et al. 2004b; Zhang et al. 2019). It exhibits a wide range of features in terms of color, texture, density, vigor, and environmental tolerance, providing it with several advantages over other species (Taliaferro 1995; Turgeon 2005). These diverse characteristics give bermudagrass an immense edge, enabling it to withstand harsh environments, compete with other species, and colonize vast disturbed areas. As such, it stands out as a truly remarkable and cosmopolitan species, adapting and thriving across some challenging environments.

The remarkable improvements in bermudagrass throughout the past century have truly revolutionized the turf industry. Bermudagrass improvement methodologies have progressed from initial prospecting for superior plants among naturally occurring variations in the germplasm to the inclusion of scientific selective breeding that began in the mid-1900s (Taliaferro et al. 2004a). The systematic development of new turf bermudagrass cultivars through breeding and selection has contributed to the development of numerous improved cultivars (Burton 1991; Taliaferro et al. 2004a; Wu and Taliaferro 2009). Incremental improvements in turf bermudagrass cultivars were achieved by turfgrass enthusiasts searching for plants with superior characteristics related to turf use (Taliaferro 1995, 2003). The development of a sterile triploid interspecific hybrid ‘Tiffine’ (Hein 1953) triggered the use of bermudagrasses on putting greens. Later, the development of another interspecific hybrid, ‘Tifgreen’, initiated the large-scale commercial use of bermudagrass on golf putting greens because of its improved putting quality, optimum leaf density, and canopy coverage at lower mowing heights (Burton 1964; Hein 1961). With the creation of ‘Tifgreen’, golf course managers saw the possibilities of having immaculate putting greens and fairways year-round. As indicated by a survey conducted in 2021, 32% of the total golf course acreage (80% of putting greens) in the southern United States adopted bermudagrass because of its stress tolerance and maintenance requirements (Emmons and Rossi 2015; Shaddox et al. 2023), thus redefining what is possible for modern turf management. Bermudagrass can survive the scorching heat of the South and also performs extremely well at low mowing heights, leading to a consistent and visually pleasing golfing experience.

The Oklahoma State University (OSU) turfgrass breeding program has been at the forefront of developing new genotypes with enhanced traits. These experimental genotypes have undergone multienvironment field trials to assess turfgrass quality, winter survivability, and drought resistance (Godwin et al. 2022). Through careful evaluation, elite selections have been chosen for patent applications and commercial release, greatly enhancing the aesthetic value and stress tolerance of these cultivars. Additionally, the identity and genetic diversity of the experimental selections have been studied, providing the opportunity to further broaden the genetic basis of the cultivars pool and exceptional value to the industry.

Previously, genetic comparisons were largely based on morphological characteristics, but they were heavily influenced by the environment. As technology continues to evolve, researchers have looked for ways to study bermudagrass more efficiently. The use of molecular markers like genomic and expressed sequence tag-derived simple sequence repeat (SSR) markers have advantages over traditional morphological comparisons by providing environmentally neutral genetic differences among genotypes and they have revolutionized how the species is studied. Many highly polymorphic, locus-specific, and reproducible SSR markers have been developed and widely used for bermudagrass (Guo et al. 2017). A set of 11 highly polymorphic SSR markers, with an average of 12.8 bands per primer pairs (PPs), were used as a reliable tool for the identification of vegetatively propagated turf bermudagrass cultivars (Wang et al. 2010). Nine SSR primer combinations amplified 88 bands and effectively detected genomic variations among bermudagrass accessions from various sources (Wang et al. 2013). A total of 1003 validated SSR PPs developed from five genomic SSR libraries were used for the development of linkage mapping and quantitative trait loci (QTL) analyses for establishment rate in common bermudagrass (Guo et al. 2017). Four SSR genomic libraries enriched with (CA)n, (GA)n, (AAG)n, and (AAT)n repeat motifs were sequenced to develop 1426 unique PPs in African bermudagrass (Tan et al. 2014b). Expressed sequence tag-derived SSR (EST-SSR) markers were also used for genetic linkage mapping of bermudagrass (Harris-Shultz et al. 2010). More recently, high-throughput single-nucleotide polymorphisms have been used to evaluate genetic diversity and identify genes in bermudagrass (Gan et al. 2022; Pudzianowska and Baird 2021; Singh et al. 2023). With the advancement of marker technology, a more precise understanding of the genetic variations within the species and environmental influences has been achieved and a foundation for molecular breeding applications in bermudagrass has been created.

As stated, a genetic base of turf bermudagrass has been broadened to allow for the development of high-quality, adaptable cultivars to varying environments and end uses. The OSU has been conducting two directional breeding efforts: one for cold-hardy cultivar development and the other for drought-resistant cultivars. The research performed at OSU included characterizing the genetic diversity of new turf bermudagrass experimental selections and comparing them to standard cultivars using SSR markers (Godwin et al. 2022). By using this directional breeding technique, we were able to determine which cultivars may be more adaptable to colder climates or require less irrigation water in drought-prone areas. Because this research is crucial for providing new turfgrass cultivars, the current study was conducted to characterize the genetic diversity and identity in a set of new turf bermudagrass experimental selections of the breeding program against standard cultivars using SSR markers.

Materials and Methods

Materials.

A total of 32 bermudagrass genotypes, comprising of 21 experimental selections and 11 cultivars, were used in this investigation (Table 1). The OSU turf bermudagrass breeding program has attempted to combine high-quality turfgrass and cold-hardiness in interspecific hybrids since the 1980s. This study included eight cold-hardy selections (OKC7018, OSU1318, OSU1433, OSU1605, OSU1656, OSU1657, OSU1675, 19x13) and six-cold hardy cultivars (Latitude 36, NorthBridge, Tahoma 31, Patriot, Tifton 10, and U3) (NTEP database at https://maps.umn.edu/ntep/) (Xiang et al. 2020). Since 2010, the OSU program received funding sponsored by the US Department of Agriculture Specialty Crop Research Initiative to develop drought-resistant interspecific hybrids. Fourteen drought-resistant selections (OKC1873, OKC1876, OSU2053, OSU2073, OSU2081, 6x2, 10x18, 12x19, 14x2, 14x16, 15x8, 15x10, 18x14) and five cultivars (Tifway, TifGrand, TifTuf, Bimini, Celebration) were included in this study (drought resistance of OKC1873, OKC1876 can be found in the NTEP database at https://maps.umn.edu/ntep/; the OSU series and number-labeled entries were observed by Serba et al. 2022).

Table 1.

List of 21 experimental selections (1–21) and 11 cultivars (22–32) of turf-type bermudagrass (Cynodon spp.) used for genotyping.

Table 1.

Genomic DNA extraction.

Healthy young leaf tissues were sampled from each of the 32 plants grown in separate pots (pot size: 15-cm diameter and 15-cm depth) in a greenhouse at the OSU Agronomy Research Station, Stillwater, OK, USA. DNA was isolated according to a modified phenol–chloroform extraction method (Fang et al. 2015; Nalini et al. 2004). DNA samples were quantified using a spectrophotometer (NanoDrop ND-1000; Thermo Fisher Scientific, Waltham, MA, USA) and diluted to 10 ng·mL−1 for the polymerase chain reaction (PCR).

SSR markers, PCR amplification, gel electrophoresis, and data analysis.

A total of 54 SSR marker PPs were used for genotyping during this study (Supplemental Table 1). Three SSR markers were selected from each of the linkage groups of common bermudagrass (except LG3, LG10, and LG13) (Guo et al. 2017). PCRs were conducted with two replications on each genotype following the procedure described by Fang et al. (2015).

Two plates of PCR products labeled with 700 and 800 nm florescent dye were pooled and loaded into a 6.5% KB Plus gel and separated by a LI-COR 4300 DNA analyzer (LI-COR, Lincoln, NE, USA). Bands that were amplified by the given SSR PPs from the gel were visually scored as present (1) or absent (0) for all samples (Supplemental Table 2). To ensure the accuracy of the results, only reproducible and consistent SSR fragments were scored. Amplified fragments of different sizes were considered as different alleles. Then, the multilocus data were transformed to a binary matrix of presence/absence of each allele for each individual genotype.

An analysis of molecular variance (AMOVA) was performed using GenALEx 6.51 (Peakall and Smouse 2012), and pairwise population differentiation (PhiPT or phi-statistics) based on standard permutation across the full data set was performed to investigate the distribution of genetic diversity within and among populations. The binary distance matrix was used as an input for the calculation by the PhiPT and AMOVA. Levels of genetic divergence between genotypes within populations were calculated using the PhiPT fixation index (Excoffier et al. 1992), and principal coordinate analyses were conducted using Nei’s genetic distance matrix and GeneAlex 6.5. The PhiPT is a measure of the relative contributions of between-population separation to the overall genetic variation in the whole sample (Tiwari et al. 2017). Estimation of the intragenotypic variation (PhiPT) distance was performed using the following formula: PhiPT = AP/(WP + AP) = AP/TOT, where AP is the estimated variation among populations, WP is the estimated variation within populations, and TOT is the sum square total (Teixeira et al. 2014), as implemented in GeneAlex 6.5.

Genetic similarity coefficients using the simple matching coefficient (Nei and Li 1979) were calculated among all possible pairs with the SIMQUAL option using NTSYS-pc version 2.2 (Rohlf 2000). The similarity matrix was used in the cluster analysis with an unweighted pair-group method with arithmetic averages (Sokal 1958) with sequential, agglomerative, hierarchical, and nested clustering algorithms (Sneath and Sokal 1973) to obtain a dendrogram. Then, a principal components analysis was performed to show the differentiation of the genotypes in a two-dimensional array of eigenvectors using the DCENTER and EIGEN modules of NTSYS-pc.

Results and Discussion

SSR marker polymorphisms.

The PCR products of 51 of the 54 SSR PPs showed good polymorphisms (Table 2). Five of the SSR markers (CDCA5-463/464, CDCA5-505/506, CDCA7-611/612, CDGA5-1363/1364, and CDATG1-1891/1892) amplified two groups of bands on the 32 genotypes, which were identified as two loci of each marker. The size of amplified fragments varied from 100 to 500 bp. A total of 352 polymorphic bands were scored for the 51 PPs used, with 3 to 13 polymorphic bands for each PP (i.e., an average of six alleles per SSR PP) (Table 2, Supplemental Table 2). Among the 51 PPs, the least amounts of polymorphisms were observed for CDCA5-473/474, CDGA3-1103/1104, CDGA2-961/962, CDCA5-461/462, and CDAAC2-2361/2362, with each having three alleles. However, CDCA7-611/612 produced 13 polymorphic bands.

Table 2.

Polymorphic bands for three simple sequence repeat marker primer pairs selected from each of the 18 linkage groups (except LG10) in common bermudagrass.

Table 2.

The SSR markers showed a high level of polymorphism among bermudagrass accessions and is touted as a reproducible marker system for genetic analyses (Godwin et al. 2022; Wang et al. 2013), varietal identification or certification and genetic diversity analyses (Fang et al. 2015, 2017; Godwin et al. 2022; Yang et al. 2018), and trait associations (Fan et al. 2020; Xie et al. 2015). The SSR markers (also called microsatellites) are ubiquitous in the genome of different higher plant species with high polymorphism, length variations, and codominant Mendelian inheritance (Morgante and Olivieri 1993; Saghai Maroof et al. 1994). The amount of genome information derived from such markers, high reproducibility, and ease of genotyping make SSR markers a good choice for plant genetic studies. Consequently, numerous EST-SSRs and genomic SSRs were developed from bermudagrass genomic resources that can be used to assess genetic diversity, assess bermudagrass genotype fingerprinting, and differentiate contaminants from cultivars (Harris-Shultz et al. 2010; Jewell et al. 2010, 2012; Kamps et al. 2011; Tan et al. 2014b). Furthermore, heterologous EST-SSRs from sugarcane became useful sources of polymorphic markers in bermudagrass that facilitated diversity analyses, genetic mapping, QTL analyses, and marker-assisted selection (Khanal et al. 2017).

Among the 352 alleles, 46 were unique to the experimental selections and 28 were unique to the cultivars. The AMOVA (Excoffier et al. 1992) showed that the variation between the two groups (experimental selections and the commercial cultivars) was only 6% of the total variation based on 352 SSR alleles. The variation within the populations accounted for 94% of the total, indicating high genetic variation among the individual genotypes in each group (Table 3). Both variations between groups and within groups were statistically significant (P < 0.05). The pairwise population differentiation (PhiPT) values were 0.012 for the cultivars and 0.169 for the selections, whereas the average for all the genotypes was 0.057 (P < 0.05). The notable genetic variations among the new experimental selections can be largely attributed to differences in origins of parental germplasm. Previous studies of 157 natural bermudagrass germplasm using sequence-related amplified polymorphism markers reported that 18% of total molecular variance was attributed to diversity among subpopulations, whereas 82% of variance was associated with differences within subpopulations (Zheng et al. 2017). A genetic differentiation analysis of 55 wild accessions of bermudagrass using SSR markers also reported that approximately 30% was attributed to among-group variations and approximately 70% was attributed to within-group variations (Ling et al. 2012). Our results are in agreement with those of previous studies of different sets of bermudagrass genotypes (Godwin et al. 2022). The higher frequency of within-group variations than that of among-group variations demonstrates the high heterozygosity attributable to the outcrossing nature of bermudagrass.

Table 3.

Analysis of molecular variance for 21 bermudagrass experimental selections and 11 commercial cultivars using 352 polymorphic bands generated by 51 simple sequence repeat primer pairs.

Table 3.

Genetic diversity and relatedness.

Genetic similarity coefficients among the 32 bermudagrass entries were analyzed using all 352 polymorphic bands. A pairwise genetic similarity coefficient estimate based on the SSR markers ranged from 0.59 to 0.89 (Table 4). The highest genetic similarity coefficient of 0.89 was observed between experimental selections OSU2053 and OSU2073 (0.89), followed by 10x18 and 12x19 (0.88). The least genetic similarity was observed between the commercial cultivars Tifton10 and TifTuf (0.59), followed by Tifton10 and TifGrand (0.60). Commercial cultivars such as U3, Tifton10, and Patriot are distantly related to the other commercial cultivars, with genetic similarity coefficients ranging from 0.60 to 0.61. Among the experimental selections, OKC7018, OSU1433, and OSU1656 showed the least genetic similarity with most of the other selections (0.62–0.67).

Table 4.

Genetic similarity of 21 bermudagrass experimental selections and 11 commercial cultivars using 352 polymorphic bands generated by 53 simple sequence repeat primer pairs.

Table 4.

The dendrogram generated using 352 polymorphic bands at a genetic similarity level of 0.70 showed six clusters comprising 32 genotypes (Fig. 1). Each of cluster #1 and cluster #2 consisted of two genotypes. ‘Patriot’ and ‘Tifton 10’, which are both cold-hardy (Taliaferro et al. 2004a), formed cluster #1, with 0.78 similarity between them, and were distantly related to the other genotypes. This result was not surprising because ‘Tifton 10’ is a parent for ‘Patriot’ (Baxter and Schwartz 2018). ‘Bimini’ and ‘Celebration’, which are relatively drought-resistant (Gopinath et al. 2022; Schiavon et al. 2021; Steinke et al. 2011), formed cluster #2, implying certain relatedness. Both the cultivars are adapted to the excessive heat and high humidity of the growing environment in Florida. Cluster #3 encompassed 13 experimental genotypes and three cultivars (Tifway, TifGrand, and TifTuf), and all of them are drought-resistant (Serba et al. 2022). The OSU experimental selections were progeny from crosses among drought-tolerant parents. Cluster #4 contained only cold-hardy U3 bermudagrass, indicating the unique genetic makeup of the cultivar. Cluster #5 included five experimental genotypes that were derived from crosses of cold-hardy parents. Cluster #6 comprised three experimental genotypes (OKC7018, OSU1318, and OSU1433) and three commercial cultivars (Latitude 36, NorthBridge, and Tahoma 31). These six genotypes shared one African bermudagrass parent but had different cold-hardy common bermudagrass parents. All the genotypes in clusters #5 and #6 are cold-hardy (Xiang et al. 2020). In Fig. 1, it is obvious that noncold-hardy genotypes were grouped in two neighboring clusters, clusters #2 and #3, whereas cold-hardy genotypes were grouped in clusters #4, #5, and #6, indicating that breeding and selection for winter survival or drought resistance has profoundly modified genetic constitutions of those progeny bermudagrasses.

Fig. 1.
Fig. 1.

An unweighted pair-group method with arithmetic averages (UPGMA) dendrogram of 21 bermudagrass experimental selections and 11 cultivars based on Jaccard similarity coefficients generated from 51 simple sequence repeat markers.

Citation: HortScience 59, 4; 10.21273/HORTSCI17525-23

Our results revealed noticeable genetic differences from those of commercial cultivars as well as genetic diversity among the experimental selections that will have the potential to broaden the genetic base if released for commercial production. A genetic diversity assessment of bermudagrass using ISSR markers reported that the degree of the relationship among the genotypes follows some degree of geographical origin and ploidy level and some morphological characteristics (Farsani et al. 2012). A study of the population structure of 296 bermudagrass genotypes from different latitudes using 153 EST-SSR markers reported more genetic diversity in higher ploidy levels and lower latitudes in China (Zhang et al. 2019), and that the population structure follows latitudes with notable admixtures and no isolation by distance. A recent genetic diversity and population structure study of common bermudagrass collection using EST-SSR markers reported notable population differentiation along longitude gradients caused by low gene flow (Zhang et al. 2021). The population structure along the latitude gradient implies the existence of genetic variation for cold adaptation among bermudagrass germplasm, whereas the longitudinal gradient implies population isolation. Evolutionary adaptation to a range of climatic and edaphic conditions contributed to the high magnitudes of genetic diversity and marker polymorphism (Wu 2011). These results imply the existence of wide genetic diversity in bermudagrass, polyploidy speciation, and adaptive evolution.

Furthermore, a principal coordinate analysis was conducted and visualized using GeneAlex 6.5. A plot of first two principal coordinates analysis axes (Fig. 2) supported the results of the cluster analysis. Hybrid selections 10x18, 12x19, 14x16, and 18x14 comprised a subpopulation. Similarly, the cultivars from Tifton, GA, USA, such as TifGrand, Tifway, and TifTuf, comprised a separate subpopulation. The other subpopulation comprised most of the OSU hybrids, such as OKC1876, OKC1873, OSU2081, OSU2073, OSU2053, 6x2, 15x8, and 15x10; these genotypes were different from the others. A large subpopulation comprising hybrid selections and cultivars was also formed. This later group can be divided into three subpopulations based on the coordinates. Because a principal coordinate analysis is a generalization of a principal component analysis, it measures the similarities and differences among the genotypes (Gower 1966).

Fig. 2.
Fig. 2.

Principal coordinate analysis of (PCA) of 21 bermudagrass experimental selections and 11 cultivars.

Citation: HortScience 59, 4; 10.21273/HORTSCI17525-23

A population structure analysis of 193 common bermudagrass and 13 African bermudagrass accessions of worldwide origin using a total of 37,496 single-nucleotide polymorphisms (called de novo) revealed four subpopulations (Singh et al. 2023). In this study, C. dactylon accessions from the OSU bermudagrass breeding program formed a separate subpopulation. This implies that the OSU breeding program has formed a unique set of bermudagrass germplasm, probably as a result of directional breeding for different stress factors.

Directional breeding specifically targets a trait or traits of interest to improve for an intended use or alleviate a priority problem. Drought resistance and winter survivability are the two important objective traits of OSU turf bermudagrass breeding. Because water is the primary input for the growth and survival of turfgrass, breeding for drought-resistant turfgrass is pivoted on water conservation by the turfgrass industry (McCarty 2010; Salaiz et al. 1991). Similarly, reduced winter injury is an important factor for hybrid bermudagrass use in the transition zone (Dunne et al. 2019; Shashikumar and Nus 1993). Therefore, developing bermudagrasses hybrids with better freeze tolerance is another key priority area for the OSU bermudagrass breeding program.

A recent study of bermudagrass germplasm for cold-hardiness and freezing tolerance under field and laboratory conditions reported a range of variability for reduced winterkill, spring green-up, and genetic sources for cold-hardiness (Dunne et al. 2019). It is obvious that the prevalence of genetic variability among germplasm determines the success of the breeding program. The molecular variability observed among the hybrid selections warranted the success of the directional breeding practice.

Correlations among the genotypes based on the 352 alleles were also assessed (Fig. 3). Three entries, OKC7018, OSU1318, and OSU1433, which formed a separate cluster, as shown in the dendrogram, were not significantly correlated with any of the experimental selections and commercial cultivars. On the contrary, OSU1605 was closely related with OSU2073, 15x8, and TifTuf. Similarly, OSU1656 was correlated with OSU2081, 14x16, and 18x14. There were also strong correlations among OSU1675, 19x13, and 14x16, and among OKC1876, OSU2081, and 15x10. Among the cultivars, NorthBridge and Bimini, Patriot and Tifton10, and Patriot and Celebration were highly correlated. TifTuf and TifGrand were correlated with some of the experimental selections. The genetic correlation can be used to describe traits in the same individual or the same trait in different individuals (Hill 2013). In our study, we assessed the correlation of genotypes with different parental background and pedigrees based on SSR marker polymorphism. The correlation matrix revealed different levels of genetic relationships among the experimental selections and with the commercial cultivars.

Fig. 3.
Fig. 3.

Correlations among 21 bermudagrass experimental selections and 11 cultivars assessed using 52 simple sequence repeat marker allele amplifications.

Citation: HortScience 59, 4; 10.21273/HORTSCI17525-23

The principal component analysis revealed the structure of covariation between the experimental selections and commercial cultivars (Fig. 4). PC1, which explained 17.8% of the variability in the data set, was driven largely by 15x10 and OSU2073. PC2 explained 11% of the variability and was largely contributed by OSU1656 and OSU2081. The analysis demonstrated how the covariation between the experimental selections and commercial cultivars were influencing the data set and revealed new insights into their relationship.

Fig. 4.
Fig. 4.

Principal component analysis (PCA) of 21 bermudagrass experimental selections and 11 cultivars using 352 polymorphic alleles amplified with 52 simple sequence repeat markers and percent contribution of different genotypes (selections with a numerical cross-identification are preceded by “X” in this figure only).

Citation: HortScience 59, 4; 10.21273/HORTSCI17525-23

The outcome of the research in this set of bermudagrass genotypes showed remarkable polymorphisms with regard to SSR markers. Clustering the accessions with the unweighted pair-group method with the arithmetic averages method and using the similarity coefficient as input formed six clusters of unique genetic signatures. Interestingly, the correlation analysis using marker data highlighted different levels of relatedness between the genotypes, which was largely attributable to their parental background. This revelation provides significant insight regarding the directional breeding and selection that have occurred in bermudagrass breeding. As the research indicates, one or more of these genotypes could contribute to the commercial cultivar pool, thus introducing even more genetic diversity.

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  • Fig. 1.

    An unweighted pair-group method with arithmetic averages (UPGMA) dendrogram of 21 bermudagrass experimental selections and 11 cultivars based on Jaccard similarity coefficients generated from 51 simple sequence repeat markers.

  • Fig. 2.

    Principal coordinate analysis of (PCA) of 21 bermudagrass experimental selections and 11 cultivars.

  • Fig. 3.

    Correlations among 21 bermudagrass experimental selections and 11 cultivars assessed using 52 simple sequence repeat marker allele amplifications.

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    Principal component analysis (PCA) of 21 bermudagrass experimental selections and 11 cultivars using 352 polymorphic alleles amplified with 52 simple sequence repeat markers and percent contribution of different genotypes (selections with a numerical cross-identification are preceded by “X” in this figure only).

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  • Gopinath L, Moss JQ, Wu Y, Schwartz BM. 2022. Drought response of 10 bermudagrass genotypes under field and controlled environment conditions. Agrosystems, Geosci Environ. 5(4):e20300. https://doi.org/10.1002/agg2.20300.

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    • Export Citation
  • Gower JC. 1966. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika. 53(3–4):325338. https://doi.org/10.1093/biomet/53.3-4.325.

    • Search Google Scholar
    • Export Citation
  • Guo Y, Wu Y, Anderson JA, Moss JQ, Zhu L, Fu J. 2017. SSR marker development, linkage mapping, and QTL analysis for establishment rate in common bermudagrass. Plant Genome. 10(1):plantgenome2016.07.0074. https://doi.org/10.3835/plantgenome2016.07.0074.

  • Hanna WW, Anderson WF. 2008. Development and impact of vegetative propagation in forage and turf bermudagrasses. Agron J. 100(S3):S-103S-107. https://doi.org/10.2134/agronj2006.0302c.

    • Search Google Scholar
    • Export Citation
  • Hanna WW, Burton GW, Johnson AW. 1990. Registration of ‘Tifton 10’ turf bermudagrass. Crop Sci. 30(6):13551356. https://doi.org/10.2135/cropsci1990.0011183X003000060041x.

    • Search Google Scholar
    • Export Citation
  • Hanna WW, Kristine Braman S, Schwartz BM. 2010. ‘ST-5’, a shade-tolerant turf bermudagrass. HortScience. 45(1):132134. https://doi.org/10.21273/hortsci.45.1.132.

    • Search Google Scholar
    • Export Citation
  • Harlan JR, de Wet JMJ. 1969. Sources of variation in Cynodon dactylon (L). Pers. Crop Sci. 9:774778.

  • Harris-Shultz KR, Schwartz BM, Hanna WW, Brady JA. 2010. Development, linkage mapping, and use of microsatellites in bermudagrass. J Am Soc Hortic Sci. 135(6):511520. https://doi.org/10.21273/JASHS.135.6.511.

    • Search Google Scholar
    • Export Citation
  • Hein MA. 1953. Registration of varieties and strains of bermuda grass, II (Cynodon dactylon (L.) Pers.). Agron J. 45(11):572573. https://doi.org/10.2134/agronj1953.00021962004500110016x.

    • Search Google Scholar
    • Export Citation
  • Hein MA. 1961. Registration of varieties and strains of bermudagrass, III. (Cynodon Dactylon (L.) Pers.). Agron J. 53(4):276. https://doi.org/10.2134/agronj1961.00021962005300040021x.

    • Search Google Scholar
    • Export Citation
  • Hill WG. 2013. Genetic correlation, p 237–239. In: Maloy S, Hughes KBT (eds). Encyclopedia of genetics (2nd ed). Academic Press, San Diego, CA, USA. https://doi.org/10.1016/B978-0-12-374984-0.00611-2.

  • Jewell MC, Frere CH, Prentis PJ, Lambrides CJ, Godwin ID. 2010. Characterization and multiplexing of EST-SSR primers in Cynodon (Poaceae) species. Am J Bot. 97(10):e99e101. https://doi.org/10.3732/ajb.1000254.

    • Search Google Scholar
    • Export Citation
  • Jewell MC, Zhou Y, Loch DS, Godwin ID, Lambrides CJ. 2012. Maximizing genetic, morphological, and geographic diversity in a core collection of Australian bermudagrass. Crop Sci. 52(2):879889. https://doi.org/10.2135/cropsci2011.09.0497.

    • Search Google Scholar
    • Export Citation
  • Kamps TL, Williams NR, Ortega VM, Chamusco KC, Harris-Shultz K, Scully BT, Chase CD. 2011. DNA polymorphisms at bermudagrass microsatellite loci and their use in genotype fingerprinting. Crop Sci. 51(3):11221131. https://doi.org/10.2135/cropsci2010.08.0478.

    • Search Google Scholar
    • Export Citation
  • Khanal S, Schwartz BM, Kim C, Adhikari J, Rainville LK, Auckland SA, Paterson AH. 2017. Cross-taxon application of sugarcane EST-SSR to genetic diversity analysis of bermudagrass (Cynodon spp.). Genet Resources Crop Evol. 64(8):20592070. https://doi.org/10.1007/s10722-017-0496-2.

    • Search Google Scholar
    • Export Citation
  • Kowalewski AR, Schwartz BM, Grimshaw AL, Sullivan DG, Peake JB. 2015. Correlations between hybrid bermudagrass morphology and wear tolerance. HortTechnology. 25(6):725730. https://doi.org/10.21273/HORTTECH.25.6.725.

    • Search Google Scholar
    • Export Citation
  • Ling Y, Zhang X-Q, Ma X, Chen S-Y, Liu W. 2012. Analysis of genetic diversity among wild bermudagrass germplasm from southwest China using SSR markers. Genet Mol Res. 11. https://doi.org/10.4238/2012.October.17.5.

    • Search Google Scholar
    • Export Citation
  • McCarty LB. 2010. Best golf course management practices: Construction, watering, fertilizing, cultural practices, and pest management strategies to maintain golf course turf with minimal environmental impact (3rd ed). Prentice Hall, Upper Saddle River, NJ, USA.

  • Morgante M, Olivieri AM. 1993. PCR-amplified microsatellites as markers in plant genetics. Plant J. 3(1):175182. https://doi.org/10.1046/j.1365-313X.1993.t01-9-00999.x.

    • Search Google Scholar
    • Export Citation
  • Nalini E, Jawali N, Bhagwat SG. 2004. A simple method for isolation of DNA from plants suitable for long-term storage and DNA marker analysis. BARC Newslett. 249.

  • Nei M, Li WH. 1979. Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc Natl Acad Sci USA. 76(10):52695273. https://doi.org/10.1073/pnas.76.10.5269.

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Supplementary Materials

Desalegn Serba US Department of Agriculture, Agriculture Research Service, US Arid-Land Agricultural Research Center, Maricopa, AZ 85138, USA

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Tilin Fang Plant and Soil Sciences Department, Oklahoma State University, Stillwater, OK 74078, USA

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Yanqi Wu Plant and Soil Sciences Department, Oklahoma State University, Stillwater, OK 74078, USA

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Contributor Notes

This research was funded in part by the US Department of Agriculture (USDA) Specialty Crop Research Initiative, US Golf Association, and USDA-Agricultural Research Service National Program 215: Pastures, Forage, and Rangeland Systems. Mention of a trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA or any part herein. The USDA is an equal opportunity provider and employer.

We declare no conflict of interest.

Y.W. is the corresponding author. E-mail: yanqi.wu@okstate.edu.

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  • Fig. 1.

    An unweighted pair-group method with arithmetic averages (UPGMA) dendrogram of 21 bermudagrass experimental selections and 11 cultivars based on Jaccard similarity coefficients generated from 51 simple sequence repeat markers.

  • Fig. 2.

    Principal coordinate analysis of (PCA) of 21 bermudagrass experimental selections and 11 cultivars.

  • Fig. 3.

    Correlations among 21 bermudagrass experimental selections and 11 cultivars assessed using 52 simple sequence repeat marker allele amplifications.

  • Fig. 4.

    Principal component analysis (PCA) of 21 bermudagrass experimental selections and 11 cultivars using 352 polymorphic alleles amplified with 52 simple sequence repeat markers and percent contribution of different genotypes (selections with a numerical cross-identification are preceded by “X” in this figure only).

 

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